How Meteorologists Forecast Weather
When you can’t blame it on the rain, you can always blame it on the Meteorologist. I cannot count how many times in my career, particularly in the beginning stages, people would watch the weather report and then approach me with, “But you said it was going to be ______!” Insert: Warm, Cold, Rainy, Snowy, Breezy, etc. The truth of the matter is, Meteorology is not an exact science. No matter how hard we try to get it exactly right, the odds of us actually doing so are virtually impossible. We can get incredibly close to nailing down the timing and intensity of incoming storms, but because there are so many variables at play, we will never get it exactly right no matter how hard we try. The good news is, thanks to Advancements in technology, this day in age we get it right a great deal more often than we get it wrong. The accuracy of computer forecasting models has greatly improved over the last fifteen-years which has helped Meteorologists become more accurate today than ever before. It is up to the Meteorologist to sift through data generated by computer models in order to forecast the most likely outcome with any storm. This becomes a difficult task when the computer models reveal large discrepancies in the forecast for a given storm. In these cases, the role of the experienced human Meteorologist is critical to determine the most likely weather scenario that will unfold.
The forecast is actually a function of a multitude variables that all need to be taken into account in order to assess what future weather conditions will occur. Current and expected temperature, dew point, humidity, barometric pressure, approaching storms, etc. all need to be investigated in order to predict what will happen next. There are a number of different methods that can be used to generate an accurate weather forecast, however this is entirely dependent on the experience level of the forecaster and the amount of data that is available to him/her. I have found that the persistence method of forecasting is most valuable in California, pending that there are no approaching storm systems or substantial changes in moisture content on the horizon. The persistence method is based on common sense. If there are no drastic changes in temperature, dew point, humidity, barometric pressure, etc. in the forecast from one day to the next, the weather conditions will likely end up being very similar both days. This approach works great in California in the Spring and Summer.